Rural domestic sewage treatment is critical for environmental protection.This study defines the spatial pattern of villages from the perspective of rural sewage treatment and develops an integrated decision-making sys...Rural domestic sewage treatment is critical for environmental protection.This study defines the spatial pattern of villages from the perspective of rural sewage treatment and develops an integrated decision-making system to propose a sewage treatment mode and scheme suitable for local conditions.By considering the village spatial layout and terrain factors,a decision tree model of residential density and terrain type was constructed with accuracies of 76.47%and 96.00%,respectively.Combined with binary classification probability unit regression,an appropriate sewage treatment mode for the village was determined with 87.00%accuracy.The Analytic Hierarchy Process(AHP),combined with the Technique for Order Preference(TOPSIS)by Similarity to an Ideal Solution model,formed the basis for optimal treatment process selection under different emission standards.Verification was conducted in 542 villages across three counties of the Inner Mongolia Autonomous Region,focusing on the standard effluent effect(0.3773),low investment cost(0.3196),and high standard effluent effect(0.5115)to determine the best treatment process for the same emission standard under different needs.The annual environmental and carbon emission benefits of sewage treatment in these villages were estimated.This model matches village density,geographic feature,and social development level,and provides scientific support and a theoretical basis for rural sewage treatment decision-making.展开更多
This study evaluated the accuracy,completeness,and comprehensibility of responses from mainstream large language models(LLMs)to hepatitis C virus(HCV)-related questions,aiming to assess their performance in addressing...This study evaluated the accuracy,completeness,and comprehensibility of responses from mainstream large language models(LLMs)to hepatitis C virus(HCV)-related questions,aiming to assess their performance in addressing patient queries about disease and lifestyle behaviors.The models selected were ChatGPT-4o,Gemini 2.0 Pro,Claude 3.5 Sonnet,and DeepSeek V3,with 12 questions chosen by two HCV experts from the domains of prevention,diagnosis,and treatment.展开更多
Global Navigation Satellite System(GNSS)observations are critical for establishing high-precision terrestrial reference frames(TRF),but the environmental loading effects,particularly hydrological loading deformation(H...Global Navigation Satellite System(GNSS)observations are critical for establishing high-precision terrestrial reference frames(TRF),but the environmental loading effects,particularly hydrological loading deformation(HYLD),remain unaccounted in existing TRF like ITRF2020,limiting their accuracy.This study evaluates the performance of multiple HYLD datasets derived from GRACE(mascon and spherical harmonic(SH)products)and four hydrological models(LSDM,ERA5,GLDAS2,and MERRA2)in explaining seasonal and non-seasonal GNSS displacements globally using IGS Repro3 and Re pro 2datasets.Among these six HYLD datasets,we demonstrate that the GRACE mascon solution achieves superior performance in explaining the seasonal and non-seasonal GNSS displacements,by quantifying the amplitude reduction ratio(AMPR)and root mean square reduction ratio(RMSR)induced by HYLD corrections,respectively.The mascon-derived HYLD achieves better correction,particularly with the vertical median AMPR of 35.1%and RMSR of 4%.In contrast,hydrological models and SH product have relatively lower performance in explaining GNSS displacements,with ERA5 achieving only 24.7%for the ve rtical AMPR.The HYLDs of coastal stations generally exhibit worse perfo rmance with lower AMPR and more negative RMSR distributions,likely reflecting the influence of ocean loading and their limitations in accurately isolating the land water signal within land boundaries;whereas the mascon result shows minimal differences between inland and coastal stations,benefitting from the reduced leakage of land water into the oceans.Furthermore,the transition from Repro2 to the improved reprocessing strategy in Re pro3 enhances the overall consistency between HYLDs and GNSS displacements,specifically with a 7%improvement in the vertical AMPR with MERRA2.展开更多
Objective To develop a depression recognition model by integrating the spirit-expression diagnostic framework of traditional Chinese medicine(TCM)with machine learning algorithms.The proposed model seeks to establish ...Objective To develop a depression recognition model by integrating the spirit-expression diagnostic framework of traditional Chinese medicine(TCM)with machine learning algorithms.The proposed model seeks to establish a TCM-informed tool for early depression screening,thereby bridging traditional diagnostic principles with modern computational approaches.Methods The study included patients with depression who visited the Shanghai Pudong New Area Mental Health Center from October 1,2022 to October 1,2023,as well as students and teachers from Shanghai University of Traditional Chinese Medicine during the same period as the healthy control group.Videos of 3–10 s were captured using a Xiaomi Pad 5,and the TCM spirit and expressions were determined by TCM experts(at least 3 out of 5 experts agreed to determine the category of TCM spirit and expressions).Basic information,facial images,and interview information were collected through a portable TCM intelligent analysis and diagnosis device,and facial diagnosis features were extracted using the Open CV computer vision library technology.Statistical analysis methods such as parametric and non-parametric tests were used to analyze the baseline data,TCM spirit and expression features,and facial diagnosis feature parameters of the two groups,to compare the differences in TCM spirit and expression and facial features.Five machine learning algorithms,including extreme gradient boosting(XGBoost),decision tree(DT),Bernoulli naive Bayes(BernoulliNB),support vector machine(SVM),and k-nearest neighbor(KNN)classification,were used to construct a depression recognition model based on the fusion of TCM spirit and expression features.The performance of the model was evaluated using metrics such as accuracy,precision,and the area under the receiver operating characteristic(ROC)curve(AUC).The model results were explained using the Shapley Additive exPlanations(SHAP).Results A total of 93 depression patients and 87 healthy individuals were ultimately included in this study.There was no statistically significant difference in the baseline characteristics between the two groups(P>0.05).The differences in the characteristics of the spirit and expressions in TCM and facial features between the two groups were shown as follows.(i)Quantispirit facial analysis revealed that depression patients exhibited significantly reduced facial spirit and luminance compared with healthy controls(P<0.05),with characteristic features such as sad expressions,facial erythema,and changes in the lip color ranging from erythematous to cyanotic.(ii)Depressed patients exhibited significantly lower values in facial complexion L,lip L,and a values,and gloss index,but higher values in facial complexion a and b,lip b,low gloss index,and matte index(all P<0.05).(iii)The results of multiple models show that the XGBoost-based depression recognition model,integrating the TCM“spirit-expression”diagnostic framework,achieved an accuracy of 98.61%and significantly outperformed four benchmark algorithms—DT,BernoulliNB,SVM,and KNN(P<0.01).(iv)The SHAP visualization results show that in the recognition model constructed by the XGBoost algorithm,the complexion b value,categories of facial spirit,high gloss index,low gloss index,categories of facial expression and texture features have significant contribution to the model.Conclusion This study demonstrates that integrating TCM spirit-expression diagnostic features with machine learning enables the construction of a high-precision depression detection model,offering a novel paradigm for objective depression diagnosis.展开更多
Realistic models for cancer research representing disease progression that commensurately respond to therapeutics consistent with clinical observation are the holy grail for pre-clinical research and screening.Althoug...Realistic models for cancer research representing disease progression that commensurately respond to therapeutics consistent with clinical observation are the holy grail for pre-clinical research and screening.Although such an ideal is elusive,well-characterized in vivo models facilitate our understanding of disease,progression,and therapeutic opportunities.Here,we characterize a commonly used syngeneic BALB/c mouse model of triple negative breast cancer(4T1)after establishing tumors in their flanks.Tumors developed at the subcutaneous injection site for all experimental mice and their volumes were monitored.We quantified a rare subset of breast cancer stemlike cells(CSCs),classified as CD44^(+)/CD24^(−)phenotypes in in vitro and ex vivo cell populations.Chromosome numbers in ex vivo metaphase cells were greater than cells cultured in vitro(89.4±3.4,range of 70-132 and 82.6±1.1,range of 70-128;respectively).Further,we observed different types of chromosome aberrations,including gap,deletion,exchange,interstitial deletion,terminal deletion,ring,dicentric,and Robertsonian translocations.For both sources of cells,the number of aberrations was dominated by deletions,terminal deletions,and Robertsonian translocations.Ex vivo cells exhibited greater prevalence of deletions and terminal deletions,whereas in vitro cells displayed more ring aberrations and Robertsonian translocations.In conclusion,we successfully characterized cancer cells from a syngeneic mouse model of breast cancer in terms of rare CSC proportion and a variety of chromosomal aberrations,which is useful for understanding tumor traits associated with cancer development and therapeutic action.The data act as a valuable resource for other studies using the 4T1 BALB/c model.展开更多
Slopes are likely to fail in areas with frequent rainfall and earthquakes.The deformation characteristics of unsaturated slopes subjected to post-rainfall earthquakes are investigated using centrifuge model tests and ...Slopes are likely to fail in areas with frequent rainfall and earthquakes.The deformation characteristics of unsaturated slopes subjected to post-rainfall earthquakes are investigated using centrifuge model tests and finite element analyses.Three tests of the slope deformation under earthquake and post-rainfall earthquakes are first studied using image analysis techniques.Then,based on an elastoplastic constitutive model,numerical simulations are carried out using the finite element method and compared with the centrifuge test results.Finally,a parametric study is performed to clarify the effects of antecedent rainfall on earthquake-induced slope deformation.The results show that slope deformation caused by post-rainfall earthquakes differs from that caused by earthquakes without antecedent rainfall.The seepage flow and soil strength of the slope are affected by previous rainfall conditions,such as intensity and duration,which directly influence the slope deformation caused by the subsequent earthquake.Soil displacement and strain become greater and the slip surface is more noticeable during the post-rainfall earthquake of higher intensity.In addition,the time interval between the rainfall and the earthquake has a considerable impact on the detailed characteristics of the slope deformation,and the significant deformation occurs at the time of lowest soil strength when seepage flow reaches the lower part of the slope.Moreover,the repeated intermittent rainfall greatly affects the subsequent earthquake-induced slope deformation,the main characteristics of which are closely related to the changes in saturation and strength of the slope.However,with the prolonged time gap between each round of rainfall,the earthquake-induced slope deformation becomes insignificant.展开更多
Accurate segmentation of breast cancer in mammogram images plays a critical role in early diagnosis and treatment planning.As research in this domain continues to expand,various segmentation techniques have been propo...Accurate segmentation of breast cancer in mammogram images plays a critical role in early diagnosis and treatment planning.As research in this domain continues to expand,various segmentation techniques have been proposed across classical image processing,machine learning(ML),deep learning(DL),and hybrid/ensemble models.This study conducts a systematic literature review using the PRISMA methodology,analyzing 57 selected articles to explore how these methods have evolved and been applied.The review highlights the strengths and limitations of each approach,identifies commonly used public datasets,and observes emerging trends in model integration and clinical relevance.By synthesizing current findings,this work provides a structured overview of segmentation strategies and outlines key considerations for developing more adaptable and explainable tools for breast cancer detection.Overall,our synthesis suggests that classical and ML methods are suitable for limited labels and computing resources,while DL models are preferable when pixel-level annotations and resources are available,and hybrid pipelines are most appropriate when fine-grained clinical precision is required.展开更多
Large language models(LLMs)have revolutionized AI applications across diverse domains.However,their widespread deployment has introduced critical security vulnerabilities,particularly prompt injection attacks that man...Large language models(LLMs)have revolutionized AI applications across diverse domains.However,their widespread deployment has introduced critical security vulnerabilities,particularly prompt injection attacks that manipulate model behavior through malicious instructions.Following Kitchenham’s guidelines,this systematic review synthesizes 128 peer-reviewed studies from 2022 to 2025 to provide a unified understanding of this rapidly evolving threat landscape.Our findings reveal a swift progression from simple direct injections to sophisticated multimodal attacks,achieving over 90%success rates against unprotected systems.In response,defense mechanisms show varying effectiveness:input preprocessing achieves 60%–80%detection rates and advanced architectural defenses demonstrate up to 95%protection against known patterns,though significant gaps persist against novel attack vectors.We identified 37 distinct defense approaches across three categories,but standardized evaluation frameworks remain limited.Our analysis attributes these vulnerabilities to fundamental LLM architectural limitations,such as the inability to distinguish instructions from data and attention mechanism vulnerabilities.This highlights critical research directions such as formal verification methods,standardized evaluation protocols,and architectural innovations for inherently secure LLM designs.展开更多
BACKGROUND:This study aims to develop and validate a machine learning-based in-hospital mortality predictive model for acute aortic syndrome(AAS)in the emergency department(ED)and to derive a simplifi ed version suita...BACKGROUND:This study aims to develop and validate a machine learning-based in-hospital mortality predictive model for acute aortic syndrome(AAS)in the emergency department(ED)and to derive a simplifi ed version suitable for rapid clinical application.METHODS:In this multi-center retrospective cohort study,AAS patient data from three hospitals were analyzed.The modeling cohort included data from the First Affiliated Hospital of Zhengzhou University and the People’s Hospital of Xinjiang Uygur Autonomous Region,with Peking University Third Hospital data serving as the external test set.Four machine learning algorithms—logistic regression(LR),multilayer perceptron(MLP),Gaussian naive Bayes(GNB),and random forest(RF)—were used to develop predictive models based on 34 early-accessible clinical variables.A simplifi ed model was then derived based on fi ve key variables(Stanford type,pericardial eff usion,asymmetric peripheral arterial pulsation,decreased bowel sounds,and dyspnea)via Least Absolute Shrinkage and Selection Operator(LASSO)regression to improve ED applicability.RESULTS:A total of 929 patients were included in the modeling cohort,and 210 were included in the external test set.Four machine learning models based on 34 clinical variables were developed,achieving internal and external validation AUCs of 0.85-0.90 and 0.73-0.85,respectively.The simplifi ed model incorporating fi ve key variables demonstrated internal and external validation AUCs of 0.71-0.86 and 0.75-0.78,respectively.Both models showed robust calibration and predictive stability across datasets.CONCLUSION:Both kinds of models were built based on machine learning tools,and proved to have certain prediction performance and extrapolation.展开更多
The study aimed at predicting potential suitable areas with national key reserve Orchidaceae plants in Heilongjiang province and conducive to plant protection.The distribution point data of six Orchidaceae plants and ...The study aimed at predicting potential suitable areas with national key reserve Orchidaceae plants in Heilongjiang province and conducive to plant protection.The distribution point data of six Orchidaceae plants and 19 bioclimatic variables were selected,and the environmental factors required for modeling were screened out by pearson correlation analysis and variance inflation factor(VIF)analysis.The potential suitable areas of Orchidaceae plants were predictat present and under different climate scenarios in 2090s by using geographic information system(GIS)and Maximum Entropy Model(MaxEnt).And then evaluated the prediction accuracy of the MaxEnt model using the AUC value,the TSS value and the Kappa value.The results showed that:1)The area under curve(AUC)values,true skill statistics(TSS)values and KAPPA values predicted by MaxEnt model were separately above 0.9,0.85 and 0.75.2)Under the climate scenario at present,the total suitable area of Orchidaceae plants was about 9.61×10^(6)km^(2),which was mainly distributed in Heilongjiang province.Among them,the high-suitable area of Cypripedium shanxiense S.C.Chen was the largest,the non-suitable area of Cypripedium guttatum Sw was the largest.3)Under different climate scenarios in 2090s,the total suitable area was slightly increasing(9.62×10^(6)km^(2)).Among them,Cypripedium shanxiense S.C.Chen and Gastrodiae Rhizoma both showed the trend of expansion to the southwest,China,and the suitable areas expanded significantly.Comprehensive factor analysis showed that temperature and precipitation were the main bioclimatic variables of suitable areas distribution,and the low emission scenario(SSP 2-4.5)will be more conducive to the survival of Orchidaceae plants.展开更多
Ion-exchange Polymer-Metal Composites(IPMCs)gain huge attentions due to large deformation,rapid electromechanical response,and high energy conversion efficiency.Deflection of IPMC arises from the volumetric swelling e...Ion-exchange Polymer-Metal Composites(IPMCs)gain huge attentions due to large deformation,rapid electromechanical response,and high energy conversion efficiency.Deflection of IPMC arises from the volumetric swelling effect induced by the concentration gradient of hydrated cations between the two electrodes,thus the volume of hydrated cation deter-mines the motion magnitude and direction of IPMC.H ion is one of the most commonly used driving cations for IPMC.However,due to its unique characteristics,particularly the inability to accurately quantify its hydration volume,existing literatures primarily focus on the physical driving models for metallic cations,i.e.,Na+,no driving model for the H ion is reported until now.This paper proposes a novel model of H ion escape from the water's body-centered cubic lattice to count the hydration volume.Number(n)of water molecules carried by the H ion is solved by combining the Lennard-Jones potential energy function with Maxwell's velocity distribution.The specific n value is equivalent to 4.04 for the H ion inside Nafion electrolyte under a 3.0 V DC electric field.Substituting it into the classic Friction Model(proposed by Tadokoro et al.at 2000),actuation behaviors of H ion driven IPMC were therefore achieved through Matlab calculations and Abaqus simulations.The calculated results of dynamic displacement and force highly match to the experimental data form the Nafion IPMC actuator driven by same electric field,showing a highly reliability of the established escape model.展开更多
While Large Language Models(LLMs)possess the capability to perform a wide range of tasks,security attacks known as prompt injection and jailbreaking remain critical challenges.Existing defense approaches addressing th...While Large Language Models(LLMs)possess the capability to perform a wide range of tasks,security attacks known as prompt injection and jailbreaking remain critical challenges.Existing defense approaches addressing this problem face challenges such as the over-refusal of prompts that contain harmful vocabulary but are semantically benign,and the limited accuracy improvement inmachine learning-based approaches due to the ease of distinguishing benign prompts in existing datasets.Therefore,we propose a multi-LLM agent framework aimed at achieving both the accurate rejection of harmful prompts and appropriate responses to benign prompts.Distinct from prior studies,the proposed method adopts In-Context Learning(ICL)during the learning phase,presenting a novel approach that obviates the need for computationally expensive parameter updates required by conventional fine-tuning.To demonstrate the proposed method’s capability for rapid and easy deployment,this study targets LLMs with insufficient alignment.In the experiments,macro-averaged binary classification metrics were used to comprehensively evaluate harmfulness detection.Experimental results using three LLMs demonstrated that the proposed method achieved performance that surpassed four baselines across all evaluation metrics for the target LLMs,evidencing significant effectiveness with an average improvement of 16.6 points in F1-score compared to the vanilla models.The significance of this study lies in the proposal of a novel approach based on ICL that does not require parameter updates.This framework offers high sustainability in practical deployment,as it allows for the adaptive enhancement of detection performance against continuously evolving attack methods solely through the accumulation of logs,without the necessity of retraining the LLM itself.By mitigating the trade-off between safety and utility,this research contributes to the implementation of robust LLMs.展开更多
The intracontinental subduction of a>200-km-long section of the Tajik-Tarim lithosphere beneath the Pamir Mountains is proposed to explain nearly 30 km of shortening in the Tajik fold-thrust belt and the Pamir upli...The intracontinental subduction of a>200-km-long section of the Tajik-Tarim lithosphere beneath the Pamir Mountains is proposed to explain nearly 30 km of shortening in the Tajik fold-thrust belt and the Pamir uplift.Seismic imaging revealed that the upper slab was scraped and that the lower slab had subducted to a depth of>150 km.These features constitute the tectonic complexity of the Pamirs,as well as the thermal subduction mechanism involved,which remains poorly understood.Hence,in this study,high-resolution three-dimensional(3D)kinematic modeling is applied to investigate the thermal structure and geometry of the subducting slab beneath the Pamirs.The modeled slab configuration reveals distinct along-strike variations,with a steeply dipping slab beneath the southern Pamirs,a more gently inclined slab beneath the northern Pamirs,and apparent upper slab termination at shallow depths beneath the Pamirs.The thermal field reveals a cold slab core after delamination,with temperatures ranging from 400℃to 800℃,enveloped by a hotter mantle reaching~1400℃.The occurrence of intermediate-depth earthquakes aligns primarily with colder slab regions,particularly near the slab tear-off below the southwestern Pamirs,indicating a strong correlation between slab temperature and seismicity.In contrast,the northern Pamirs exhibit reduced seismicity at depth,which is likely associated with thermal weakening and delamination.The central Pamirs show a significant thermal anomaly caused by a concave slab,where the coldest crust does not descend deeply,further suggesting crustal detachment or mechanical failure.The lateral asymmetry in slab temperature possibly explains the mechanism of lateral tearing and differential slab-mantle coupling.展开更多
The 1739 M8.0 Pingluo earthquake occurred around the Yinchuan Graben,bounded by the Helan Mountains to the west and the Ordos Block to the east.Seismological observations have shown that surface fault displacement rea...The 1739 M8.0 Pingluo earthquake occurred around the Yinchuan Graben,bounded by the Helan Mountains to the west and the Ordos Block to the east.Seismological observations have shown that surface fault displacement reaches about 2–3 m,mainly by dip-slip motion along the Helanshan Piedmont Fault.However,the documented seismic intensity is distributed predominantly within the basin area,exhibiting a sharp asymmetry across the Helanshan Piedmont Fault.Thus,the general pattern of earthquake faulting is still under debate.We built a three-dimensional elastodynamic finiteelement model to reappraise the fault mechanism.In the model,predictions from synthetic rupture models,based on available observations and the earthquake scaling law,were used as an input with the split-node technique,and the effect of basin sediment on elastic wave propagation was considered.The numerical results show that if an earthquake occurred on the Helanshan Piedmont Fault characterized by a high-angle(70°)normal fault,earthquake shaking,as predicted from the modeled peak ground velocity and peak ground acceleration,has difficulty fitting the observed result,even when the effect of sediment amplification is considered.To better fit the observed shaking pattern,the dip angle of the Helanshan Piedmont Fault must be less than about 35°between the depths of about 8–27 km,where the coseismic slip may reach about 6 m.This result leads us to conclude that the 1739 M8.0 great earthquake likely occurred on a listric normal fault at depth,in agreement with the geometry of the Helanshan Piedmont Fault,as recently evidenced by seismic reflection explorations.This conclusion means that in an intracontinental setting,a reduction in the fault dip angle along the subsurface could increase the width of the fault in the elastic crust,making misalignment between the surface rupture and the isoseismals and resulting in an increase in the upper bound of earthquake magnitude relative to simple high-angle faulting.展开更多
The outstanding growth in the applications of large language models(LLMs)demonstrates the significance of adaptive and efficient prompt engineering tactics.The existing methods may not be variable,vigorous and streaml...The outstanding growth in the applications of large language models(LLMs)demonstrates the significance of adaptive and efficient prompt engineering tactics.The existing methods may not be variable,vigorous and streamlined in different domains.The offered study introduces an immediate optimization outline,named PROMPTx-PE,that is going to yield a greater level of precision and strength when it comes to the assignments that are premised on LLM.The proposed systemfeatures a timely selection schemewhich is informed by reinforcement learning,a contextual layer and a dynamic weighting module which is regulated by Lyapunov-based stability guidelines.The PROMPTx-PE dynamically varies the exploration and exploitation of the prompt space,depending on real-time feedback and multi-objective reward development.Extensive testing on both benchmark(GLUE,SuperGLUE)and domain-specific data(Healthcare-QA and Industrial-NER)demonstrates a large best performance to be 89.4%and a strong robustness disconnect with under 3%computation expense.The results confirm the effectiveness,consistency,and scalability of PROMPTx-PE as a platform of adaptive prompt engineering based on recent uses of LLMs.展开更多
Existing methods for tracing water pollution sources typically integrate three-dimensional excitationemission matrix(3D-EEM)fluorescence spectroscopy with similarity-based matching algorithms.However,these approaches ...Existing methods for tracing water pollution sources typically integrate three-dimensional excitationemission matrix(3D-EEM)fluorescence spectroscopy with similarity-based matching algorithms.However,these approaches exhibit high error rates in borderline cases and necessitate expert manual review,which limits scalability and introduces inconsistencies between algorithmic outputs and expert judgment.To address these limitations,we propose a large vision-language model(VLM)designed as an“expert agent”to automatically refine similarity scores,ensuring alignment with expert decisions and overcoming key application bottlenecks.The model consists of two core components:(1)rule-based similarity calculation module generate initial spectral similarity scores,and(2)pre-trained large vision-language model fine-tuned via supervised learning and reinforcement learning with human feedback(RLHF)to emulate expert assessments.To facilitate training and evaluation,we introduce two expert-annotated datasets,Spec1k and SpecReason,which capture both quantitative corrections and qualitative reasoning patterns,allowing the model to emulate expert decision-making processes.Experimental results demonstrate that our method achieves 81.45%source attribution accuracy,38.24%higher than rule-based and machine learning baselines.Real-world deployment further validates its effectiveness.展开更多
In the lush heart of Uganda’s Busoga sub-region,Isaac Imaka is charting a new course for rural development.After seven years in national media,he left the newsroom and stepped into the soil.The former reporter with t...In the lush heart of Uganda’s Busoga sub-region,Isaac Imaka is charting a new course for rural development.After seven years in national media,he left the newsroom and stepped into the soil.The former reporter with the Daily Monitor was driven by the belief that communities like his in Jinja North deserved more than chronic poverty and hand-to-mouth survival.展开更多
Traditional source-to-sink analyses cannot effectively characterize deep-time sedimentary processes involving multiple sediment sources and the spatiotemporal evolution of sediment contributions from different sources...Traditional source-to-sink analyses cannot effectively characterize deep-time sedimentary processes involving multiple sediment sources and the spatiotemporal evolution of sediment contributions from different sources.In this study,a dynamic,quantitative source-to-sink analysis approach using stratigraphic forward modeling(SFM)is proposed,and it is applied to the Paleogene Enping Formation in the Baiyun Sag,Pearl River Mouth Basin.The built-in spatiotemporal provenance tagging of the model assigns a unique time-source label to sediments from each provenance,making each source's contribution identifiably“labeled”in the simulated formation,and thus enabling a direct precise tracking and high spatiotemporal resolution quantification of such contributions.Five pseudo-wells(from proximal to distal locations)in the Baiyun Sag were analyzed.The simulation results quantitatively represent the varied proportion of contribution of each source at different locations and in different periods and verify the proposed approach's operability and accuracy of the proposed approach.The simulated 3D deposit distribution shows a high agreement with the measured stratigraphic data,validating the model's reliability.Results reveal significant spatiotemporal changes in the Enping sedimentary system.In the late stage of Enping Formation deposition,a distal source supply from the northern part of the sag became dominant,the depocenter migrated northward to the deepwater area,and large-scale deltaic sand bodies extensively progradating into the sag were formed.The modeled 3D deposit distribution indicates that extensive high-quality reservoir sandstones are likely present across the deepwater area of the Baiyun Sag,which are identified as key exploration targets.Compared to traditional static approaches,the SFM-based dynamic simulation markedly enhances the spatiotemporal resolution of source-to-sink analysis and quantitatively captures the sedimentary system's responses to tectonic activity,base-level fluctuations and other external drivers.The proposed approach provides a novel quantitative framework for investigating complex,deep-time,multi-source systems,and offers an effective tool for reservoir prediction and hydrocarbon exploration planning in underexplored deepwater areas.展开更多
Myasthenia gravis is a chronic autoimmune disorder that affects the neuromuscular junction leading to fluctuating skeletal muscle fatigability. The majority of myasthenia gravis patients have detectable antibodies in ...Myasthenia gravis is a chronic autoimmune disorder that affects the neuromuscular junction leading to fluctuating skeletal muscle fatigability. The majority of myasthenia gravis patients have detectable antibodies in their serum, targeting acetylcholine receptor, muscle-specific kinase, or related proteins. Current treatment for myasthenia gravis involves symptomatic therapy, immunosuppressive drugs such as corticosteroids, azathioprine, and mycophenolate mofetil, and thymectomy, which is primarily indicated in patients with thymoma or thymic hyperplasia. However, this condition continues to pose significant challenges including an unpredictable and variable disease progression, differing response to individual therapies, and substantial longterm side effects associated with standard treatments(including an increased risk of infections, osteoporosis, and diabetes), underscoring the necessity for a more personalized approach to treatment. Furthermore, about fifteen percent of patients, called “refractory myasthenia gravis patients”, do not respond adequately to standard therapies. In this context, the introduction of molecular therapies has marked a significant advance in myasthenia gravis management. Advances in understanding myasthenia gravis pathogenesis, especially the role of pathogenic antibodies, have driven the development of these biological drugs, which offer more selective, rapid, and safer alternatives to traditional immunosuppressants. This review aims to provide a comprehensive overview of emerging therapeutic strategies targeting specific immune pathways in myasthenia gravis, with a particular focus on preclinical evidence, therapeutic rationale, and clinical translation of B-cell depletion therapies, neonatal Fc receptor inhibitors, and complement inhibitors.展开更多
African drylands occupied 19.6 million km~2(46%of the total global area)and 525 million people.Soil erosion models are useful for assessing the impact of soil erosion in the dryland areas.This review provides an asses...African drylands occupied 19.6 million km~2(46%of the total global area)and 525 million people.Soil erosion models are useful for assessing the impact of soil erosion in the dryland areas.This review provides an assessment of soil erosion/deposition models and soil conservation practices,which are supportive for mitigating the impact of soil erosion and maintaining soil health and soil functional services for food security in African drylands.The theories of soil erosion models and soil conservation practices provide advanced ways to understand the detailed impact of soil erosion and management solutions.The paper reviews a set of useful soil erosion models and traditional conservation practices,which can control soil erosion and enhance dryland farming systems in Africa.Soil erosion models are classified into three categories:empirical,conceptual,and physical.Soil conservation practices include reduced tillage,advanced cover crops,mechanical structures(barriers made of stones/gravel/vegetation),advanced mechanical roller-crimper technique,mixed cropping,intercropping,crop rotation systems,terracing techniques,and land modification techniques.These conservation practices are effective in controlling soil erosion,reducing soil damage,improving soil health and quality,enhancing soil fertility,and ensuring food security.The existing assessment suggests that understanding the theories of soil erosion models and soil conservation practices is a first step towards addressing soil erosion problems in African drylands.展开更多
基金supported by the Central Government Guiding Local Science and Technology Development Fund Project(No.2024SZY0343)the Joint Research Program for Ecological Conservation and High Quality Development of the Yellow River Basin(No.2022-YRUC-01-050205)+2 种基金the Higher Education Scientific Research Project of Inner Mongolia Autonomous Region(No.NJZZ23078)the project of Inner Mongolia"Prairie Talents"Engineering Innovation Entrepreneurship Talent Team,the Major Projects of Erdos Science and Technology(No.2022EEDSKJZDZX015)the Innovation Team of the Inner Mongolia Academy of Science and Technology(No.CXTD2023-01-016).
文摘Rural domestic sewage treatment is critical for environmental protection.This study defines the spatial pattern of villages from the perspective of rural sewage treatment and develops an integrated decision-making system to propose a sewage treatment mode and scheme suitable for local conditions.By considering the village spatial layout and terrain factors,a decision tree model of residential density and terrain type was constructed with accuracies of 76.47%and 96.00%,respectively.Combined with binary classification probability unit regression,an appropriate sewage treatment mode for the village was determined with 87.00%accuracy.The Analytic Hierarchy Process(AHP),combined with the Technique for Order Preference(TOPSIS)by Similarity to an Ideal Solution model,formed the basis for optimal treatment process selection under different emission standards.Verification was conducted in 542 villages across three counties of the Inner Mongolia Autonomous Region,focusing on the standard effluent effect(0.3773),low investment cost(0.3196),and high standard effluent effect(0.5115)to determine the best treatment process for the same emission standard under different needs.The annual environmental and carbon emission benefits of sewage treatment in these villages were estimated.This model matches village density,geographic feature,and social development level,and provides scientific support and a theoretical basis for rural sewage treatment decision-making.
基金funded by the National Key Research and Development Program of China(No.2021YFA1100500)the National Natural Science Foundation of China(No.82370662)the Key Research&Development Plan of Zhejiang Province(No.2024C03051).
文摘This study evaluated the accuracy,completeness,and comprehensibility of responses from mainstream large language models(LLMs)to hepatitis C virus(HCV)-related questions,aiming to assess their performance in addressing patient queries about disease and lifestyle behaviors.The models selected were ChatGPT-4o,Gemini 2.0 Pro,Claude 3.5 Sonnet,and DeepSeek V3,with 12 questions chosen by two HCV experts from the domains of prevention,diagnosis,and treatment.
基金sponsored by the National Natural Science Foundation of China,China(Grant Nos.42174028,42474030,and 41774007)Major Science and Technology Program of Hubei Province,China(Grant No.JSCX202501188)the Natural Science Foundation of Wuhan,China(Grant No.2024040701010029)。
文摘Global Navigation Satellite System(GNSS)observations are critical for establishing high-precision terrestrial reference frames(TRF),but the environmental loading effects,particularly hydrological loading deformation(HYLD),remain unaccounted in existing TRF like ITRF2020,limiting their accuracy.This study evaluates the performance of multiple HYLD datasets derived from GRACE(mascon and spherical harmonic(SH)products)and four hydrological models(LSDM,ERA5,GLDAS2,and MERRA2)in explaining seasonal and non-seasonal GNSS displacements globally using IGS Repro3 and Re pro 2datasets.Among these six HYLD datasets,we demonstrate that the GRACE mascon solution achieves superior performance in explaining the seasonal and non-seasonal GNSS displacements,by quantifying the amplitude reduction ratio(AMPR)and root mean square reduction ratio(RMSR)induced by HYLD corrections,respectively.The mascon-derived HYLD achieves better correction,particularly with the vertical median AMPR of 35.1%and RMSR of 4%.In contrast,hydrological models and SH product have relatively lower performance in explaining GNSS displacements,with ERA5 achieving only 24.7%for the ve rtical AMPR.The HYLDs of coastal stations generally exhibit worse perfo rmance with lower AMPR and more negative RMSR distributions,likely reflecting the influence of ocean loading and their limitations in accurately isolating the land water signal within land boundaries;whereas the mascon result shows minimal differences between inland and coastal stations,benefitting from the reduced leakage of land water into the oceans.Furthermore,the transition from Repro2 to the improved reprocessing strategy in Re pro3 enhances the overall consistency between HYLDs and GNSS displacements,specifically with a 7%improvement in the vertical AMPR with MERRA2.
基金General Program of National Natural Science Foundation of China(82474390)Construction Project of Pudong New Area Famous TCM Studios(National Pilot Zone for TCM Development,Shanghai)(PDZY-2025-0716)Shanghai Municipal Science and Technology Program Project Shanghai Key Laboratory of Health Identification and Assessment(21DZ2271000).
文摘Objective To develop a depression recognition model by integrating the spirit-expression diagnostic framework of traditional Chinese medicine(TCM)with machine learning algorithms.The proposed model seeks to establish a TCM-informed tool for early depression screening,thereby bridging traditional diagnostic principles with modern computational approaches.Methods The study included patients with depression who visited the Shanghai Pudong New Area Mental Health Center from October 1,2022 to October 1,2023,as well as students and teachers from Shanghai University of Traditional Chinese Medicine during the same period as the healthy control group.Videos of 3–10 s were captured using a Xiaomi Pad 5,and the TCM spirit and expressions were determined by TCM experts(at least 3 out of 5 experts agreed to determine the category of TCM spirit and expressions).Basic information,facial images,and interview information were collected through a portable TCM intelligent analysis and diagnosis device,and facial diagnosis features were extracted using the Open CV computer vision library technology.Statistical analysis methods such as parametric and non-parametric tests were used to analyze the baseline data,TCM spirit and expression features,and facial diagnosis feature parameters of the two groups,to compare the differences in TCM spirit and expression and facial features.Five machine learning algorithms,including extreme gradient boosting(XGBoost),decision tree(DT),Bernoulli naive Bayes(BernoulliNB),support vector machine(SVM),and k-nearest neighbor(KNN)classification,were used to construct a depression recognition model based on the fusion of TCM spirit and expression features.The performance of the model was evaluated using metrics such as accuracy,precision,and the area under the receiver operating characteristic(ROC)curve(AUC).The model results were explained using the Shapley Additive exPlanations(SHAP).Results A total of 93 depression patients and 87 healthy individuals were ultimately included in this study.There was no statistically significant difference in the baseline characteristics between the two groups(P>0.05).The differences in the characteristics of the spirit and expressions in TCM and facial features between the two groups were shown as follows.(i)Quantispirit facial analysis revealed that depression patients exhibited significantly reduced facial spirit and luminance compared with healthy controls(P<0.05),with characteristic features such as sad expressions,facial erythema,and changes in the lip color ranging from erythematous to cyanotic.(ii)Depressed patients exhibited significantly lower values in facial complexion L,lip L,and a values,and gloss index,but higher values in facial complexion a and b,lip b,low gloss index,and matte index(all P<0.05).(iii)The results of multiple models show that the XGBoost-based depression recognition model,integrating the TCM“spirit-expression”diagnostic framework,achieved an accuracy of 98.61%and significantly outperformed four benchmark algorithms—DT,BernoulliNB,SVM,and KNN(P<0.01).(iv)The SHAP visualization results show that in the recognition model constructed by the XGBoost algorithm,the complexion b value,categories of facial spirit,high gloss index,low gloss index,categories of facial expression and texture features have significant contribution to the model.Conclusion This study demonstrates that integrating TCM spirit-expression diagnostic features with machine learning enables the construction of a high-precision depression detection model,offering a novel paradigm for objective depression diagnosis.
基金National Research,Development and Innovation Fund of the Ministry of Culture and Innovation under the National Laboratories Program(National Tumor Biology Laboratory,Grant/Award Number:2022-2.1.1-NL-2022-00010)Senior Research Fellowship from National Health and Medical Research Council of Australia,Grant/Award Number:1156693+1 种基金Hungarian Thematic Excellence Program,Grant/Award Number:TKP2021-EGA-44Tour de Cure,Pioneering Grant,Grant/Award Number:RSP-253-18/19。
文摘Realistic models for cancer research representing disease progression that commensurately respond to therapeutics consistent with clinical observation are the holy grail for pre-clinical research and screening.Although such an ideal is elusive,well-characterized in vivo models facilitate our understanding of disease,progression,and therapeutic opportunities.Here,we characterize a commonly used syngeneic BALB/c mouse model of triple negative breast cancer(4T1)after establishing tumors in their flanks.Tumors developed at the subcutaneous injection site for all experimental mice and their volumes were monitored.We quantified a rare subset of breast cancer stemlike cells(CSCs),classified as CD44^(+)/CD24^(−)phenotypes in in vitro and ex vivo cell populations.Chromosome numbers in ex vivo metaphase cells were greater than cells cultured in vitro(89.4±3.4,range of 70-132 and 82.6±1.1,range of 70-128;respectively).Further,we observed different types of chromosome aberrations,including gap,deletion,exchange,interstitial deletion,terminal deletion,ring,dicentric,and Robertsonian translocations.For both sources of cells,the number of aberrations was dominated by deletions,terminal deletions,and Robertsonian translocations.Ex vivo cells exhibited greater prevalence of deletions and terminal deletions,whereas in vitro cells displayed more ring aberrations and Robertsonian translocations.In conclusion,we successfully characterized cancer cells from a syngeneic mouse model of breast cancer in terms of rare CSC proportion and a variety of chromosomal aberrations,which is useful for understanding tumor traits associated with cancer development and therapeutic action.The data act as a valuable resource for other studies using the 4T1 BALB/c model.
基金supported by the China Postdoctoral Science Foundation(CPSF)(Grant No.2024M762769)the Natural Science Basic Research Program of Shaanxi(Grant No.2024JC-YBQN-0333)the Postdoctoral Fellowship Program of CPSF(Grant No.GZC20232230).
文摘Slopes are likely to fail in areas with frequent rainfall and earthquakes.The deformation characteristics of unsaturated slopes subjected to post-rainfall earthquakes are investigated using centrifuge model tests and finite element analyses.Three tests of the slope deformation under earthquake and post-rainfall earthquakes are first studied using image analysis techniques.Then,based on an elastoplastic constitutive model,numerical simulations are carried out using the finite element method and compared with the centrifuge test results.Finally,a parametric study is performed to clarify the effects of antecedent rainfall on earthquake-induced slope deformation.The results show that slope deformation caused by post-rainfall earthquakes differs from that caused by earthquakes without antecedent rainfall.The seepage flow and soil strength of the slope are affected by previous rainfall conditions,such as intensity and duration,which directly influence the slope deformation caused by the subsequent earthquake.Soil displacement and strain become greater and the slip surface is more noticeable during the post-rainfall earthquake of higher intensity.In addition,the time interval between the rainfall and the earthquake has a considerable impact on the detailed characteristics of the slope deformation,and the significant deformation occurs at the time of lowest soil strength when seepage flow reaches the lower part of the slope.Moreover,the repeated intermittent rainfall greatly affects the subsequent earthquake-induced slope deformation,the main characteristics of which are closely related to the changes in saturation and strength of the slope.However,with the prolonged time gap between each round of rainfall,the earthquake-induced slope deformation becomes insignificant.
基金funded by BK21 FOUR(Fostering Outstanding Universities for Research)(No.:5199990914048).
文摘Accurate segmentation of breast cancer in mammogram images plays a critical role in early diagnosis and treatment planning.As research in this domain continues to expand,various segmentation techniques have been proposed across classical image processing,machine learning(ML),deep learning(DL),and hybrid/ensemble models.This study conducts a systematic literature review using the PRISMA methodology,analyzing 57 selected articles to explore how these methods have evolved and been applied.The review highlights the strengths and limitations of each approach,identifies commonly used public datasets,and observes emerging trends in model integration and clinical relevance.By synthesizing current findings,this work provides a structured overview of segmentation strategies and outlines key considerations for developing more adaptable and explainable tools for breast cancer detection.Overall,our synthesis suggests that classical and ML methods are suitable for limited labels and computing resources,while DL models are preferable when pixel-level annotations and resources are available,and hybrid pipelines are most appropriate when fine-grained clinical precision is required.
基金supported by 2023 Higher Education Scientific Research Planning Project of China Society of Higher Education(No.23PG0408)2023 Philosophy and Social Science Research Programs in Jiangsu Province(No.2023SJSZ0993)+2 种基金Nantong Science and Technology Project(No.JC2023070)Key Project of Jiangsu Province Education Science 14th Five-Year Plan(Grant No.B-b/2024/02/41)the Open Fund of Advanced Cryptography and System Security Key Laboratory of Sichuan Province(Grant No.SKLACSS-202407).
文摘Large language models(LLMs)have revolutionized AI applications across diverse domains.However,their widespread deployment has introduced critical security vulnerabilities,particularly prompt injection attacks that manipulate model behavior through malicious instructions.Following Kitchenham’s guidelines,this systematic review synthesizes 128 peer-reviewed studies from 2022 to 2025 to provide a unified understanding of this rapidly evolving threat landscape.Our findings reveal a swift progression from simple direct injections to sophisticated multimodal attacks,achieving over 90%success rates against unprotected systems.In response,defense mechanisms show varying effectiveness:input preprocessing achieves 60%–80%detection rates and advanced architectural defenses demonstrate up to 95%protection against known patterns,though significant gaps persist against novel attack vectors.We identified 37 distinct defense approaches across three categories,but standardized evaluation frameworks remain limited.Our analysis attributes these vulnerabilities to fundamental LLM architectural limitations,such as the inability to distinguish instructions from data and attention mechanism vulnerabilities.This highlights critical research directions such as formal verification methods,standardized evaluation protocols,and architectural innovations for inherently secure LLM designs.
基金supported by the special fund of the National Clinical Key Specialty Construction Program[(2022)301-2305].
文摘BACKGROUND:This study aims to develop and validate a machine learning-based in-hospital mortality predictive model for acute aortic syndrome(AAS)in the emergency department(ED)and to derive a simplifi ed version suitable for rapid clinical application.METHODS:In this multi-center retrospective cohort study,AAS patient data from three hospitals were analyzed.The modeling cohort included data from the First Affiliated Hospital of Zhengzhou University and the People’s Hospital of Xinjiang Uygur Autonomous Region,with Peking University Third Hospital data serving as the external test set.Four machine learning algorithms—logistic regression(LR),multilayer perceptron(MLP),Gaussian naive Bayes(GNB),and random forest(RF)—were used to develop predictive models based on 34 early-accessible clinical variables.A simplifi ed model was then derived based on fi ve key variables(Stanford type,pericardial eff usion,asymmetric peripheral arterial pulsation,decreased bowel sounds,and dyspnea)via Least Absolute Shrinkage and Selection Operator(LASSO)regression to improve ED applicability.RESULTS:A total of 929 patients were included in the modeling cohort,and 210 were included in the external test set.Four machine learning models based on 34 clinical variables were developed,achieving internal and external validation AUCs of 0.85-0.90 and 0.73-0.85,respectively.The simplifi ed model incorporating fi ve key variables demonstrated internal and external validation AUCs of 0.71-0.86 and 0.75-0.78,respectively.Both models showed robust calibration and predictive stability across datasets.CONCLUSION:Both kinds of models were built based on machine learning tools,and proved to have certain prediction performance and extrapolation.
基金funded by Project of Scientific Research Business Expenses of Provincial Scientific Research Institutes in Heilongjiang Province(No.CZKYF2023-1-B024)Heilongjiang Academy of Sciences Dean Fund Project(No.YZ2022ZR02)+1 种基金the Science and Technology Basic Resources Investigation Program of China(No.2019FY100500)the Fundamental Research Funds for the Central Universities(No.2572023CT11).
文摘The study aimed at predicting potential suitable areas with national key reserve Orchidaceae plants in Heilongjiang province and conducive to plant protection.The distribution point data of six Orchidaceae plants and 19 bioclimatic variables were selected,and the environmental factors required for modeling were screened out by pearson correlation analysis and variance inflation factor(VIF)analysis.The potential suitable areas of Orchidaceae plants were predictat present and under different climate scenarios in 2090s by using geographic information system(GIS)and Maximum Entropy Model(MaxEnt).And then evaluated the prediction accuracy of the MaxEnt model using the AUC value,the TSS value and the Kappa value.The results showed that:1)The area under curve(AUC)values,true skill statistics(TSS)values and KAPPA values predicted by MaxEnt model were separately above 0.9,0.85 and 0.75.2)Under the climate scenario at present,the total suitable area of Orchidaceae plants was about 9.61×10^(6)km^(2),which was mainly distributed in Heilongjiang province.Among them,the high-suitable area of Cypripedium shanxiense S.C.Chen was the largest,the non-suitable area of Cypripedium guttatum Sw was the largest.3)Under different climate scenarios in 2090s,the total suitable area was slightly increasing(9.62×10^(6)km^(2)).Among them,Cypripedium shanxiense S.C.Chen and Gastrodiae Rhizoma both showed the trend of expansion to the southwest,China,and the suitable areas expanded significantly.Comprehensive factor analysis showed that temperature and precipitation were the main bioclimatic variables of suitable areas distribution,and the low emission scenario(SSP 2-4.5)will be more conducive to the survival of Orchidaceae plants.
基金National Natural Science Foundations of China(52275295)Central Plains Science and Technology Innovation Leading Talents(234200510026).
文摘Ion-exchange Polymer-Metal Composites(IPMCs)gain huge attentions due to large deformation,rapid electromechanical response,and high energy conversion efficiency.Deflection of IPMC arises from the volumetric swelling effect induced by the concentration gradient of hydrated cations between the two electrodes,thus the volume of hydrated cation deter-mines the motion magnitude and direction of IPMC.H ion is one of the most commonly used driving cations for IPMC.However,due to its unique characteristics,particularly the inability to accurately quantify its hydration volume,existing literatures primarily focus on the physical driving models for metallic cations,i.e.,Na+,no driving model for the H ion is reported until now.This paper proposes a novel model of H ion escape from the water's body-centered cubic lattice to count the hydration volume.Number(n)of water molecules carried by the H ion is solved by combining the Lennard-Jones potential energy function with Maxwell's velocity distribution.The specific n value is equivalent to 4.04 for the H ion inside Nafion electrolyte under a 3.0 V DC electric field.Substituting it into the classic Friction Model(proposed by Tadokoro et al.at 2000),actuation behaviors of H ion driven IPMC were therefore achieved through Matlab calculations and Abaqus simulations.The calculated results of dynamic displacement and force highly match to the experimental data form the Nafion IPMC actuator driven by same electric field,showing a highly reliability of the established escape model.
基金supported by JSPS KAKENHI Grant Numbers JP23K28377,JP24H00714,JP25K15109,JP25K03190,JP25K03232,JP22K12157The Telecommunications Advancement Foundation.
文摘While Large Language Models(LLMs)possess the capability to perform a wide range of tasks,security attacks known as prompt injection and jailbreaking remain critical challenges.Existing defense approaches addressing this problem face challenges such as the over-refusal of prompts that contain harmful vocabulary but are semantically benign,and the limited accuracy improvement inmachine learning-based approaches due to the ease of distinguishing benign prompts in existing datasets.Therefore,we propose a multi-LLM agent framework aimed at achieving both the accurate rejection of harmful prompts and appropriate responses to benign prompts.Distinct from prior studies,the proposed method adopts In-Context Learning(ICL)during the learning phase,presenting a novel approach that obviates the need for computationally expensive parameter updates required by conventional fine-tuning.To demonstrate the proposed method’s capability for rapid and easy deployment,this study targets LLMs with insufficient alignment.In the experiments,macro-averaged binary classification metrics were used to comprehensively evaluate harmfulness detection.Experimental results using three LLMs demonstrated that the proposed method achieved performance that surpassed four baselines across all evaluation metrics for the target LLMs,evidencing significant effectiveness with an average improvement of 16.6 points in F1-score compared to the vanilla models.The significance of this study lies in the proposal of a novel approach based on ICL that does not require parameter updates.This framework offers high sustainability in practical deployment,as it allows for the adaptive enhancement of detection performance against continuously evolving attack methods solely through the accumulation of logs,without the necessity of retraining the LLM itself.By mitigating the trade-off between safety and utility,this research contributes to the implementation of robust LLMs.
基金the Chinese Academy of Sciences Pioneer Hundred Talents Program and the Second Tibetan Plateau Scientific Expedition and Research Program(Grant No.2019QZKK0708)supported by a MEXT(Ministry of Education,Culture,Sports,Science and Technology)KAKENHI(Grants-in-Aid for Scientific Research)grant(Grant No.21H05203)Kobe University Strategic International Collaborative Research Grant(Type B Fostering Joint Research).
文摘The intracontinental subduction of a>200-km-long section of the Tajik-Tarim lithosphere beneath the Pamir Mountains is proposed to explain nearly 30 km of shortening in the Tajik fold-thrust belt and the Pamir uplift.Seismic imaging revealed that the upper slab was scraped and that the lower slab had subducted to a depth of>150 km.These features constitute the tectonic complexity of the Pamirs,as well as the thermal subduction mechanism involved,which remains poorly understood.Hence,in this study,high-resolution three-dimensional(3D)kinematic modeling is applied to investigate the thermal structure and geometry of the subducting slab beneath the Pamirs.The modeled slab configuration reveals distinct along-strike variations,with a steeply dipping slab beneath the southern Pamirs,a more gently inclined slab beneath the northern Pamirs,and apparent upper slab termination at shallow depths beneath the Pamirs.The thermal field reveals a cold slab core after delamination,with temperatures ranging from 400℃to 800℃,enveloped by a hotter mantle reaching~1400℃.The occurrence of intermediate-depth earthquakes aligns primarily with colder slab regions,particularly near the slab tear-off below the southwestern Pamirs,indicating a strong correlation between slab temperature and seismicity.In contrast,the northern Pamirs exhibit reduced seismicity at depth,which is likely associated with thermal weakening and delamination.The central Pamirs show a significant thermal anomaly caused by a concave slab,where the coldest crust does not descend deeply,further suggesting crustal detachment or mechanical failure.The lateral asymmetry in slab temperature possibly explains the mechanism of lateral tearing and differential slab-mantle coupling.
基金Natural Science Foundation of China(No.42120104004)。
文摘The 1739 M8.0 Pingluo earthquake occurred around the Yinchuan Graben,bounded by the Helan Mountains to the west and the Ordos Block to the east.Seismological observations have shown that surface fault displacement reaches about 2–3 m,mainly by dip-slip motion along the Helanshan Piedmont Fault.However,the documented seismic intensity is distributed predominantly within the basin area,exhibiting a sharp asymmetry across the Helanshan Piedmont Fault.Thus,the general pattern of earthquake faulting is still under debate.We built a three-dimensional elastodynamic finiteelement model to reappraise the fault mechanism.In the model,predictions from synthetic rupture models,based on available observations and the earthquake scaling law,were used as an input with the split-node technique,and the effect of basin sediment on elastic wave propagation was considered.The numerical results show that if an earthquake occurred on the Helanshan Piedmont Fault characterized by a high-angle(70°)normal fault,earthquake shaking,as predicted from the modeled peak ground velocity and peak ground acceleration,has difficulty fitting the observed result,even when the effect of sediment amplification is considered.To better fit the observed shaking pattern,the dip angle of the Helanshan Piedmont Fault must be less than about 35°between the depths of about 8–27 km,where the coseismic slip may reach about 6 m.This result leads us to conclude that the 1739 M8.0 great earthquake likely occurred on a listric normal fault at depth,in agreement with the geometry of the Helanshan Piedmont Fault,as recently evidenced by seismic reflection explorations.This conclusion means that in an intracontinental setting,a reduction in the fault dip angle along the subsurface could increase the width of the fault in the elastic crust,making misalignment between the surface rupture and the isoseismals and resulting in an increase in the upper bound of earthquake magnitude relative to simple high-angle faulting.
基金supported by the National Science and Technology Council(NSTC),Taiwan,under grant number 114-2221-E-182-041-MY3by Chang Gung University and Chang Gung Memorial Hospital under project number NERPD4Q0021.
文摘The outstanding growth in the applications of large language models(LLMs)demonstrates the significance of adaptive and efficient prompt engineering tactics.The existing methods may not be variable,vigorous and streamlined in different domains.The offered study introduces an immediate optimization outline,named PROMPTx-PE,that is going to yield a greater level of precision and strength when it comes to the assignments that are premised on LLM.The proposed systemfeatures a timely selection schemewhich is informed by reinforcement learning,a contextual layer and a dynamic weighting module which is regulated by Lyapunov-based stability guidelines.The PROMPTx-PE dynamically varies the exploration and exploitation of the prompt space,depending on real-time feedback and multi-objective reward development.Extensive testing on both benchmark(GLUE,SuperGLUE)and domain-specific data(Healthcare-QA and Industrial-NER)demonstrates a large best performance to be 89.4%and a strong robustness disconnect with under 3%computation expense.The results confirm the effectiveness,consistency,and scalability of PROMPTx-PE as a platform of adaptive prompt engineering based on recent uses of LLMs.
文摘Existing methods for tracing water pollution sources typically integrate three-dimensional excitationemission matrix(3D-EEM)fluorescence spectroscopy with similarity-based matching algorithms.However,these approaches exhibit high error rates in borderline cases and necessitate expert manual review,which limits scalability and introduces inconsistencies between algorithmic outputs and expert judgment.To address these limitations,we propose a large vision-language model(VLM)designed as an“expert agent”to automatically refine similarity scores,ensuring alignment with expert decisions and overcoming key application bottlenecks.The model consists of two core components:(1)rule-based similarity calculation module generate initial spectral similarity scores,and(2)pre-trained large vision-language model fine-tuned via supervised learning and reinforcement learning with human feedback(RLHF)to emulate expert assessments.To facilitate training and evaluation,we introduce two expert-annotated datasets,Spec1k and SpecReason,which capture both quantitative corrections and qualitative reasoning patterns,allowing the model to emulate expert decision-making processes.Experimental results demonstrate that our method achieves 81.45%source attribution accuracy,38.24%higher than rule-based and machine learning baselines.Real-world deployment further validates its effectiveness.
文摘In the lush heart of Uganda’s Busoga sub-region,Isaac Imaka is charting a new course for rural development.After seven years in national media,he left the newsroom and stepped into the soil.The former reporter with the Daily Monitor was driven by the belief that communities like his in Jinja North deserved more than chronic poverty and hand-to-mouth survival.
基金Supported by the National Natural Science Foundation of China(92055204)Strategic Priority Research Program of the Chinese Academy of Sciences(Class A)(XDA14010401)China National Offshore Oil Corporation(CNOOC)(CCL2021SKPS0118)。
文摘Traditional source-to-sink analyses cannot effectively characterize deep-time sedimentary processes involving multiple sediment sources and the spatiotemporal evolution of sediment contributions from different sources.In this study,a dynamic,quantitative source-to-sink analysis approach using stratigraphic forward modeling(SFM)is proposed,and it is applied to the Paleogene Enping Formation in the Baiyun Sag,Pearl River Mouth Basin.The built-in spatiotemporal provenance tagging of the model assigns a unique time-source label to sediments from each provenance,making each source's contribution identifiably“labeled”in the simulated formation,and thus enabling a direct precise tracking and high spatiotemporal resolution quantification of such contributions.Five pseudo-wells(from proximal to distal locations)in the Baiyun Sag were analyzed.The simulation results quantitatively represent the varied proportion of contribution of each source at different locations and in different periods and verify the proposed approach's operability and accuracy of the proposed approach.The simulated 3D deposit distribution shows a high agreement with the measured stratigraphic data,validating the model's reliability.Results reveal significant spatiotemporal changes in the Enping sedimentary system.In the late stage of Enping Formation deposition,a distal source supply from the northern part of the sag became dominant,the depocenter migrated northward to the deepwater area,and large-scale deltaic sand bodies extensively progradating into the sag were formed.The modeled 3D deposit distribution indicates that extensive high-quality reservoir sandstones are likely present across the deepwater area of the Baiyun Sag,which are identified as key exploration targets.Compared to traditional static approaches,the SFM-based dynamic simulation markedly enhances the spatiotemporal resolution of source-to-sink analysis and quantitatively captures the sedimentary system's responses to tectonic activity,base-level fluctuations and other external drivers.The proposed approach provides a novel quantitative framework for investigating complex,deep-time,multi-source systems,and offers an effective tool for reservoir prediction and hydrocarbon exploration planning in underexplored deepwater areas.
文摘Myasthenia gravis is a chronic autoimmune disorder that affects the neuromuscular junction leading to fluctuating skeletal muscle fatigability. The majority of myasthenia gravis patients have detectable antibodies in their serum, targeting acetylcholine receptor, muscle-specific kinase, or related proteins. Current treatment for myasthenia gravis involves symptomatic therapy, immunosuppressive drugs such as corticosteroids, azathioprine, and mycophenolate mofetil, and thymectomy, which is primarily indicated in patients with thymoma or thymic hyperplasia. However, this condition continues to pose significant challenges including an unpredictable and variable disease progression, differing response to individual therapies, and substantial longterm side effects associated with standard treatments(including an increased risk of infections, osteoporosis, and diabetes), underscoring the necessity for a more personalized approach to treatment. Furthermore, about fifteen percent of patients, called “refractory myasthenia gravis patients”, do not respond adequately to standard therapies. In this context, the introduction of molecular therapies has marked a significant advance in myasthenia gravis management. Advances in understanding myasthenia gravis pathogenesis, especially the role of pathogenic antibodies, have driven the development of these biological drugs, which offer more selective, rapid, and safer alternatives to traditional immunosuppressants. This review aims to provide a comprehensive overview of emerging therapeutic strategies targeting specific immune pathways in myasthenia gravis, with a particular focus on preclinical evidence, therapeutic rationale, and clinical translation of B-cell depletion therapies, neonatal Fc receptor inhibitors, and complement inhibitors.
基金part of the project on soil and water management approved and supported by the Department of Agronomy,Nasarawa State University,Keffi(NSUK),Nigeria。
文摘African drylands occupied 19.6 million km~2(46%of the total global area)and 525 million people.Soil erosion models are useful for assessing the impact of soil erosion in the dryland areas.This review provides an assessment of soil erosion/deposition models and soil conservation practices,which are supportive for mitigating the impact of soil erosion and maintaining soil health and soil functional services for food security in African drylands.The theories of soil erosion models and soil conservation practices provide advanced ways to understand the detailed impact of soil erosion and management solutions.The paper reviews a set of useful soil erosion models and traditional conservation practices,which can control soil erosion and enhance dryland farming systems in Africa.Soil erosion models are classified into three categories:empirical,conceptual,and physical.Soil conservation practices include reduced tillage,advanced cover crops,mechanical structures(barriers made of stones/gravel/vegetation),advanced mechanical roller-crimper technique,mixed cropping,intercropping,crop rotation systems,terracing techniques,and land modification techniques.These conservation practices are effective in controlling soil erosion,reducing soil damage,improving soil health and quality,enhancing soil fertility,and ensuring food security.The existing assessment suggests that understanding the theories of soil erosion models and soil conservation practices is a first step towards addressing soil erosion problems in African drylands.
